Ignite Creation Date:
2024-10-26 @ 3:38 PM
Last Modification Date:
2024-10-26 @ 3:38 PM
Study NCT ID:
NCT06566534
Status:
RECRUITING
Last Update Posted:
None
First Post:
2024-08-20
Brief Title:
Personalized Nudging to Increase Influenza Vaccinations
Sponsor:
None
Organization:
None
Study Overview
Official Title:
A Prospective Randomized Trial of Personalized Nudges to Increase Influenza Vaccinations
Status:
RECRUITING
Status Verified Date:
2024-10
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
The purpose of this study is to prospectively test whether personalized message-based nudges can increase flu vaccination compared with nudges that are not personalized or no nudges
Detailed Description:
On average 8 of the US population gets sick from influenza each flu season Since 2010 the annual disease burden of influenza in the US has included 9-41 million illnesses 140000-710000 hospitalizations and 12000-52000 deaths The Centers for Disease Control and Prevention CDC recommends flu vaccination to everyone aged 6 months and older with rare exceptions almost anyone can benefit from the vaccine which can reduce illnesses missed work hospitalizations and death
Successful efforts to get patients vaccinated against influenza have included text message reminders timed to precede upcoming flu shot-eligible appointments by up to 3 days For example the Roybal-funded flu shot megastudy conducted with Penn Medicine and Geisinger patients assessed the effectiveness of numerous types of messages in increasing vaccination relative to standard communications by the respective health systems an average 21 percentage point absolute increase or 5 relative increase in flu shots occurred due to the messages Similarly follow-up analysis of the megastudy using machine learning revealed that interventions differed in relative effectiveness across groups of patients as a function of overlapping covariates eg age sex insurance type and comorbidities This analysis found that nudges optimally targeted to subgroups who responded most strongly to those nudges in the megastudy would have resulted in up to three times the increases in vaccination observed when simply randomly assigning patients to messages
The present study aims to prospectively test the efficacy of a patient-facing message-based nudge via short message service SMS texts that is predicted by this retrospective machine learning algorithm to be most effective for them Personalized Nudge relative to Passive Control no messages Active Control simple reminder message and Best Nudge best performing message from the 2020 megastudy Patients will be randomized to one of these four arms
Of the 19 original messages from the megastudy 12 can be carried out at Geisinger in 2024 the other 7 are either no longer relevant eg those that refer to an ongoing coronavirus pandemic or cannot be carried out due to a technical limitation eg Geisinger is unable to send pictures so nudges with pictures are excluded A treatment assignment tree based on the algorithm described above will be applied to this subset of nudges to generate rules for assigning patients to personalized messages based on observed covariates
The last patients will be enrolled on December 28th for appointments scheduled on December 31st At least 90000 patients are expected to be enrolled
Successful efforts to get patients vaccinated against influenza have included text message reminders timed to precede upcoming flu shot-eligible appointments by up to 3 days For example the Roybal-funded flu shot megastudy conducted with Penn Medicine and Geisinger patients assessed the effectiveness of numerous types of messages in increasing vaccination relative to standard communications by the respective health systems an average 21 percentage point absolute increase or 5 relative increase in flu shots occurred due to the messages Similarly follow-up analysis of the megastudy using machine learning revealed that interventions differed in relative effectiveness across groups of patients as a function of overlapping covariates eg age sex insurance type and comorbidities This analysis found that nudges optimally targeted to subgroups who responded most strongly to those nudges in the megastudy would have resulted in up to three times the increases in vaccination observed when simply randomly assigning patients to messages
The present study aims to prospectively test the efficacy of a patient-facing message-based nudge via short message service SMS texts that is predicted by this retrospective machine learning algorithm to be most effective for them Personalized Nudge relative to Passive Control no messages Active Control simple reminder message and Best Nudge best performing message from the 2020 megastudy Patients will be randomized to one of these four arms
Of the 19 original messages from the megastudy 12 can be carried out at Geisinger in 2024 the other 7 are either no longer relevant eg those that refer to an ongoing coronavirus pandemic or cannot be carried out due to a technical limitation eg Geisinger is unable to send pictures so nudges with pictures are excluded A treatment assignment tree based on the algorithm described above will be applied to this subset of nudges to generate rules for assigning patients to personalized messages based on observed covariates
The last patients will be enrolled on December 28th for appointments scheduled on December 31st At least 90000 patients are expected to be enrolled
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None